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To date, the healthcare industry has had limited success in developing treatments for rare diseases. Reassessing their approach to drug discovery and development has opened the door to artificial intelligence (AI) and machine learning (ML). AI and ML offer augmented approaches and healthcare is already using such technology to support diagnosis and optimising patient treatment in a number of settings. “If you think of the digital data that we have access to now, and if AI and ML can be used together with human expertise to recognise patterns between disease pathophysiology, symptoms, targets, and drugs, the possibilities are enormous,” says Krishnan Nandabalan CEO of InveniAi, who seek to transform access to innovation for healthcare and other industries.

The difference between AI and ML

AI and ML are often spoken about in
the same breath, but there are nuanced differences between the two. AI focuses on
using computers to do jobs that are typically done by humans and accelerating
the rate and accuracy with which they can be performed. ML, on the other hand,
is really about developing algorithms and models to help computers identify patterns
to predict outcomes.

Fewer patients with rare diseases means they are hard to research

Nandabalan believes the applications of both AI and ML can go further than diagnostics, and that they hold particular promise for the development of treatments for rare diseases. “Only about 10% of drugs that make it to clinical trials are successful. For rare diseases it’s half or even a third of that,” he says. “The reason we have less success is the fact that there are fewer patients, so it’s much harder to gain the insights you need to develop treatments.”

Only about 10% of drugs that make it to clinical trials are successful. For rare diseases it’s half or even a third of that.

Without these key insights, issues of
risk and safety become stumbling blocks. This is where AI and ML can help to bridge
the gap. ML offers the capability to take data from a whole range of sources
and uncover common threads, hidden connections and other patterns that give
fresh insight into possible treatments and the impact they could have on
patients.

Uncovering hidden connections and insights by using AI and ML

While no drugs have come to market as
a result of using AI and ML as yet, Nandabalan believes it’s only a matter of
time. “As more and more data is being accumulated, AI and ML can uncover new
insights,” he says.

Speaking of his company’s own research he continues: “We have already used AI and ML to uncover hidden connections between basic biological processes and pathophysiology that allowed us to identify existing drugs that can be used to treat common and rare diseases. For example, our technology identified BXCL701, a DPP8/9 and FAP inhibitor, as an immuno-oncology candidate that is expected to enter Phase Ib/II trials for treatment-emergent neuroendocrine prostate cancer (tNEPC), a rare hormone-refractory segment of prostate cancer.”

Treatment concept to approval can take 10 years

At present it takes around 10 years to
take a new treatment from concept through to approval. Nandabalan believes this
time could be halved with the application of AI and ML, especially when
repositioning drugs. With a reduction in timescales costs would also fall,
which is so often a limiting factor in the development process and certainly a major
setback for rare diseases where there are already limited therapeutic options.

While it can be easy to overhype technology,
many believe AI and ML are the next step in the evolution of the drug discovery
and development process. Big pharmaceutical companies are investing in the
technology and, with gathering momentum, all the signs suggest that big
breakthroughs are just around the corner.